Development of a Computer Vision-Based Road Physical Feature Extraction

Livability assessment plays a crucial role in urban planning, development, and maintenance. However, the lack of database on the complex physical attributes of city roads of developing countries hinders practical analysis and interpretation of livability indicators. To address this gap, this paper proposes a computer vision-based road physical feature extraction system which aims to automate the collection and annotation of road physical attributes. Our proposed work utilizes modules for lane and object detection to automate data collection. By instating this system, urban planners and policymakers could gain valuable information for making decisions regarding infrastructure and road maintenance. The core object detection of the system achieved an average precision, recall, and mAP of 0.72, 0.46, and 0.52, respectively. For both bike lane detection and road lane counting, the average accuracy achieved is 0.64, and the average mean absolute error achieved is 0.37. Our proposed work provided a computer vision approach to detecting bike lines and counting road lanes capable of storing and visualizing the extracted road physical features.